Name
Abstract | ||
|---|---|---|
This paper presents the system description of Machine Translation (MT) system(s) for Indic Languages Multilingual Task for the 2018 edition of the WAT Shared Task. In our experiments, we (the RGNLP team) explore both statistical and neural methods across all language pairs. (We further present an extensive comparison of language-related problems for both the approaches in the context of low-resourced settings.) Our PBSMT models were highest score on all automatic evaluation metrics in the English into Telugu, Hindi, Bengali, Tamil portion of the shared task. |
Year | Venue | Field |
|---|---|---|
2018 | WAT@PACLIC | Tamil,Hindi,Computer science,Machine translation,Bengali,Artificial intelligence,Natural language processing,Telugu |
DocType | Volume | Citations |
Journal | abs/1812.00798 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Atul Kr. Ojha | 1 | 2 | 4.38 |
| Koel Dutta Chowdhury | 2 | 0 | 2.37 |
| Chao-Hong Liu | 3 | 0 | 1.35 |
| Karan Saxena | 4 | 0 | 0.34 |